Open AccessTackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE
Author(s) -
Aries Saifudin,
Harco Leslie Hendric Spits Warnars,
Benfano Soewito,
Ford Lumban Gaol,
Edi Abdurachman,
Yaya Heryadi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/662/6/062011
Subject(s) - oversampling , adaboost , computer science , machine learning , ensemble learning , class (philosophy) , artificial intelligence , software , data mining , software bug , pattern recognition (psychology) , support vector machine , computer network , bandwidth (computing) , programming language
The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassification. The ensemble technique using AdaBoost and Bagging algorithms. The results of the study show that the model that integrates SMOTE and Bagging provides better performance. The proposed model can find more software defects and more precise.